An Integrative Clustering Approach Combining Particle Swarm Optimization and Formal Concept Analysis

In this article we propose an integrative clustering approach for analysis of gene expression data across multiple experiments, based on Particle Swarm Optimization (PSO) and Formal Concept Analysis (FCA). In the proposed algorithm, the available microarray experiments are initially divided into groups of related datasets with respect to a predefined criterion. Subsequently, a hybrid clustering algorithm, based on PSO and k-means clustering, is applied to each group of experiments separately. This produces a list of different clustering solutions, one per each group. These clustering solutions are pooled together and further analyzed by employing FCA which allows to extract valuable insights from the data and generate a gene partition over the whole set of experiments. The performance of the proposed clustering algorithm is evaluated on time series expression data obtained from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe. The obtained experimental results demonstrate that the proposed integrative algorithm allows to generate a unique and robust gene partition over several different microarray datasets.